3 research outputs found
Multivariable Iterative Learning Control Design Procedures: from Decentralized to Centralized, Illustrated on an Industrial Printer
Iterative Learning Control (ILC) enables high control performance through
learning from measured data, using only limited model knowledge in the form of
a nominal parametric model. Robust stability requires robustness to modeling
errors, often due to deliberate undermodeling. The aim of this paper is to
develop a range of approaches for multivariable ILC, where specific attention
is given to addressing interaction. The proposed methods either address the
interaction in the nominal model, or as uncertainty, i.e., through robust
stability. The result is a range of techniques, including the use of the
structured singular value (SSV) and Gershgorin bounds, that provide a different
trade-off between modeling requirements, i.e., modeling effort and cost, and
achievable performance. This allows control engineers to select the approach
that fits the modeling budget and control requirements. This trade-off is
demonstrated in a case study on an industrial flatbed printer
Design and modeling aspects in multivariable iterative learning control
Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation study. Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation stud
Design and modeling aspects in multivariable iterative learning control
Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation study